Incremental Label Distribution Learning with Scalable Graph Convolutional Networks

• URL: http://arxiv.org/abs/2411.13097v1
• Date: Wed, 20 Nov 2024 07:49:51 GMT
• Title: Incremental Label Distribution Learning with Scalable Graph Convolutional Networks
• Authors: Ziqi Jia, Xiaoyang Qu, Chenghao Liu, Jianzong Wang,
• Abstract summary: We introduce Incremental Label Distribution Learning (ILDL), analyze its key issues regarding training samples and inter-label relationships. Specifically, we develop a New-label-aware Gradient Compensation Loss to speed up the learning of new labels and represent inter-label relationships as a graph.
• Score: 41.02170058889797
• License:
• Abstract: Label Distribution Learning (LDL) is an effective approach for handling label ambiguity, as it can analyze all labels at once and indicate the extent to which each label describes a given sample. Most existing LDL methods consider the number of labels to be static. However, in various LDL-specific contexts (e.g., disease diagnosis), the label count grows over time (such as the discovery of new diseases), a factor that existing methods overlook. Learning samples with new labels directly means learning all labels at once, thus wasting more time on the old labels and even risking overfitting the old labels. At the same time, learning new labels by the LDL model means reconstructing the inter-label relationships. How to make use of constructed relationships is also a crucial challenge. To tackle these challenges, we introduce Incremental Label Distribution Learning (ILDL), analyze its key issues regarding training samples and inter-label relationships, and propose Scalable Graph Label Distribution Learning (SGLDL) as a practical framework for implementing ILDL. Specifically, in SGLDL, we develop a New-label-aware Gradient Compensation Loss to speed up the learning of new labels and represent inter-label relationships as a graph to reduce the time required to reconstruct inter-label relationships. Experimental results on the classical LDL dataset show the clear advantages of unique algorithms and illustrate the importance of a dedicated design for the ILDL problem.

Related papers

• Towards Better Performance in Incomplete LDL: Addressing Data Imbalance [48.54894491724677]
  We propose textIncomplete and Imbalance Label Distribution Learning (I(2)LDL), a framework that simultaneously handles incomplete labels and imbalanced label distributions. Our method decomposes the label distribution matrix into a low-rank component for frequent labels and a sparse component for rare labels, effectively capturing the structure of both head and tail labels.
  arXiv  Detail & Related papers  (2024-10-17T14:12:57Z)
• Inaccurate Label Distribution Learning with Dependency Noise [52.08553913094809]
  We introduce the Dependent Noise-based Inaccurate Label Distribution Learning (DN-ILDL) framework to tackle the challenges posed by noise in label distribution learning. We show that DN-ILDL effectively addresses the ILDL problem and outperforms existing LDL methods.
  arXiv  Detail & Related papers  (2024-05-26T07:58:07Z)
• Exploiting Multi-Label Correlation in Label Distribution Learning [0.0]
  Label Distribution Learning (LDL) is a novel machine learning paradigm that assigns label distribution to each instance. Recent studies disclosed that label distribution matrices are typically full-rank, posing challenges to works exploiting low-rank label correlation. We introduce an auxiliary MLL process in LDL and capture low-rank label correlation on that MLL rather than LDL.
  arXiv  Detail & Related papers  (2023-08-03T13:06:45Z)
• Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations [91.67511167969934]
  imprecise label learning (ILL) is a framework for the unification of learning with various imprecise label configurations. We demonstrate that ILL can seamlessly adapt to partial label learning, semi-supervised learning, noisy label learning, and, more importantly, a mixture of these settings.
  arXiv  Detail & Related papers  (2023-05-22T04:50:28Z)
• Contrastive Label Enhancement [13.628665406039609]
  We propose Contrastive Label Enhancement (ConLE) to generate high-level features by contrastive learning strategy. We leverage the obtained high-level features to gain label distributions through a welldesigned training strategy.
  arXiv  Detail & Related papers  (2023-05-16T14:53:07Z)
• Label Distribution Learning from Logical Label [19.632157794117553]
  Label distribution learning (LDL) is an effective method to predict the label description degree (a.k.a. label distribution) of a sample. But annotating label distribution for training samples is extremely costly. We propose a novel method to learn an LDL model directly from the logical label, which unifies LE and LDL into a joint model.
  arXiv  Detail & Related papers  (2023-03-13T04:31:35Z)
• One Positive Label is Sufficient: Single-Positive Multi-Label Learning with Label Enhancement [71.9401831465908]
  We investigate single-positive multi-label learning (SPMLL) where each example is annotated with only one relevant label. A novel method named proposed, i.e., Single-positive MultI-label learning with Label Enhancement, is proposed. Experiments on benchmark datasets validate the effectiveness of the proposed method.
  arXiv  Detail & Related papers  (2022-06-01T14:26:30Z)
• Knowledge-Guided Multi-Label Few-Shot Learning for General Image Recognition [75.44233392355711]
  KGGR framework exploits prior knowledge of statistical label correlations with deep neural networks. It first builds a structured knowledge graph to correlate different labels based on statistical label co-occurrence. Then, it introduces the label semantics to guide learning semantic-specific features. It exploits a graph propagation network to explore graph node interactions.
  arXiv  Detail & Related papers  (2020-09-20T15:05:29Z)
• Generalized Label Enhancement with Sample Correlations [24.582764493585362]
  We propose two novel label enhancement methods, i.e., Label Enhancement with Sample Correlations (LESC) and generalized Label Enhancement with Sample Correlations (gLESC) Benefitting from the sample correlations, the proposed methods can boost the performance of label enhancement.
  arXiv  Detail & Related papers  (2020-04-07T03:32:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.